Techniques for collecting, managing, and providing real-time or near real-time relevant information have been enhanced through the use of the Internet and online research and information collection tools. One such set of tools is known as web analytics. Web analytics focuses on a company's own website for collection of online information, particularly traffic data. Web analytics are limited in that they only consider a subset of the relevant online universe, specifically the behavior of users of a given website.
Other analytics tools try to learn and predict the exposure and reach of advertisements displayed on websites, including social media websites. These tools gather statistics related to the reach and exposure of the advertisements. The statistics may include the number of impressions, URLs of webpages displaying the advertisements, geographical locations of users that watched the advertisements, click-through rate of advertisements, the period of time that each viewer watched the advertisements, and so on.
Currently, every ad-serving company as well as each social media website independently gathers its own statistics and analytics with regard to the exposure and reach of advertisements. However, campaign managers who like to have better understanding about the reach of advertisements and whether their budget was well spent have limited tools by which to do so. As a result, campaign managers cannot efficiently analyze and understand the performance of an advertisement campaign.
Specifically, the information gathered by a single ad-serving company or a social website per campaign may include trillions of records. Multiplying these by different companies serving the same campaigns makes it almost impossible for campaign managers to analyze the gathered information using existing tools. Further, in addition to the volume of the gathered information, each ad-serving company presents the gathered statistics using a different format. This further increases the complexity of the campaign analysis.
It should be noted that failing to efficiently and accurately analyze the performance of an advertising campaign results in revenue losses for businesses, as their advertising budget is not being efficiently spent.
Additionally, existing user level database solutions typically utilize cookies (or any type of identifiers) received from each ad-serving company (such as, e.g., a social media website, ad-serving systems, and the like) to determine user identities. Each ad-serving company or website normally uses its own unique identifier (user ID) to mark the end user. As a result, it is probable that the same end-user accessing advertisements via multiple ad-serving companies and/or social media websites can be mapped to numerous users in the user level database. This multiple mapping can create misleading data, thereby resulting in loss of information or conspicuous inconsistencies in data.
As an example, a user may view an advertisement for Coca Cola® on both Facebook® and Twitter®. The Coca Cola® company may wish to determine the reach of its advertising campaign by determining how many users viewed its campaigns across various media platforms. With respect to this user, Facebook® and Twitter® have stored different user IDs (e.g., in a form of cookies) for the same user. When Coca Cola® seeks to generate a user level database to track how many people have viewed its advertising campaign, that user may be marked in the user level database twice. When this scenario occurs respective of many users, the data ceases to be truly reflective of the number of people who actually viewed the campaign.
Moreover, the inconsistency in the application level user data would prevent campaign managers from deriving accurate and meaningful analytics respective of their campaigns. For example, post-impression or post-conversion data can be analyzed. As another example, campaign managers cannot properly assess the effectiveness of each of the media platform campaign running the campaign.
It would therefore be advantageous to provide a solution that would overcome the deficiencies of the prior art by generating unified user level data across a variety of different media platforms.